Disclosure Limitation and Confidentiality Protection in Linked Data

John Abowd (), Ian Schmutte and Lars Vilhuber

Working Papers from U.S. Census Bureau, Center for Economic Studies

Abstract: Confidentiality protection for linked administrative data is a combination of access modalities and statistical disclosure limitation. We review traditional statistical disclosure limitation methods and newer methods based on synthetic data, input noise infusion and formal privacy. We discuss how these methods are integrated with access modalities by providing three detailed examples. The first example is the linkages in the Health and Retirement Study to Social Security Administration data. The second example is the linkage of the Survey of Income and Program Participation to administrative data from the Internal Revenue Service and the Social Security Administration. The third example is the Longitudinal Employer-Household Dynamics data, which links state unemployment insurance records for workers and firms to a wide variety of censuses and surveys at the U.S. Census Bureau. For examples, we discuss access modalities, disclosure limitation methods, the effectiveness of those methods, and the resulting analytical validity. The final sections discuss recent advances in access modalities for linked administrative data.

Pages: 38 pages
Date: 2018-01
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https://www2.census.gov/ces/wp/2018/CES-WP-18-07.pdf First version, 2018 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:cen:wpaper:18-07

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